Data-Driven Analytics Leveraging Artificial Intelligence in theEra of COVID-19: An Insightful Review of Recent Developments
Abdul Majeed * and Seong Oun Hwang *
Department of Computer Engineering, Gachon University, Seongnam 13120, Korea* Correspondence: [email protected] (A.M.); [email protected] (S.O.H.); Tel.: +82-31-750-5327 (S.O.H.)
Abstract: This paper presents the role of artificial intelligence (AI) and other latest technologies thatwere employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). Thesetechnologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcareburden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablersof these technologies was data that was obtained from heterogeneous sources (i.e., social networks(SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiologicalinvestigations, and other digital/sensing platforms). To this end, we provide an insightful overview ofthe role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss majorservices that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI rolein seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)),(ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role inperforming analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AIrole in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applicationsof AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligningprotein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template)against COVID-19. Further, we discuss the challenges involved in applying AI to the available dataand privacy issues that can arise from personal data transitioning into cyberspace. We also provide aconcise overview of other latest technologies that were increasingly applied to limit the spread of theongoing pandemic. Finally, we discuss the avenues of future research in the respective area. Thisinsightful review aims to highlight existing AI-based technological developments and future researchdynamics in this area.
The recent pandemic of novel coronavirus disease 2019 (COVID-19) has changed ourlives into a new normal where free-mobility, social gatherings at a large scale, and travelingseem impossible for the next couple of years. The COVID-19 has forced the closure ofmany cities and borders for a prolonged period of time. Furthermore, changes in thebusiness/work hours and operating procedures of most organizations have completelychanged. One of the biggest religious gatherings of the world at Mecca was cancelledor scaled down due to the pandemic last year [1]. Specifically, the whole world is goingthrough an unanticipated and extraordinary challenge of COVID-19 [2]. Although there isa bright hope in terms of vaccine to end this pandemic, its distribution to underprivilegedcountries is a main challenge. Furthermore, rehabilitation of the healthcare system to payample attention to other existing diseases is also one of the main challenges in the nearfuture [3]. In the absence of potential vaccine, one of the main technologies that played acritical role in combating the pandemic is artificial intelligence (AI) [4]. It can help in curbingthe disease spread through contact tracing, social distancing, quarantine monitoring, trendsanalysis, symptoms reporting and analysis, symptoms clustering, symptoms severity
estimation, disease spread modeling, and alerting. We explain these services in detail inSection 3. A generic overview of application areas where AI has already demonstrated itseffectiveness are shown in Figure 1. This study demonstrates AI applications in the contextof the COVID-19 pandemic.
Figure 1. Overview of application areas of AI (Ref. [5]).
The impact and efficacy of AI techniques and models have been reported by manycountries in curbing the disease spread. AI models have helped to identify the transmissionroutes of this disease and helped in mitigating the disease [6]. Further, the adoption of AIaided in recovering the economies from the low-levels with improved policies [7]. Theadoption level of AI in each country was different. We present the latest finding aboutAI use with real-data until November, 2020 [8] and synthetic data from November, 2020onward in the top ten countries across the globe in Figure 2.
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Figure 2. Histogram of data about the use of AI techniques in ten countries (Ref. [8]).
From the results provided in Figure 2, it can be seen that the adoption of AI washigher in China, and this is the first country that curbed the pandemic spread quickly [9,10].Besides the higher use of AI during this pandemic, the adoption of technical mechanisms(e.g., automated decision support systems, AI-driven diagnosis, and mobile doctors etc.)in the healthcare sector are relatively higher in China compared to other countries [11,12].Therefore, AI-powered healthcare systems as well as other rigorous measures helped Chinato contain the spread of COVID-19 quickly. Furthermore, in those countries that adoptedAI techniques at a smaller scale, the pandemic forced the closure of many facilities andactivities [13]. Although AI played a vital role in this pandemic, many barriers were therein the adoption of AI, such as privacy and data manipulation, etc. In Figure 2, we chosena sample of ten representative countries based on the origin of the pandemic, severity ofdisease, higher daily cases tally, digitization in the healthcare sector, and/or COVID-19
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variants reported. In some countries (i.e., India, Pakistan, and Bangladesh), the covid caseswere relatively higher, but adoption of the digital mechanisms in all these countries issignificantly lower. That is why we did not include those countries in the analysis.
There are several studies that have covered this topic, especially AI role in the ongoingCOVID-19 pandemic [14–18]. However, these studies have covered only general servicesof AI in COVID-19 context, and the main emphasis of most studies was on digital surveil-lance (or contact tracing). Furthermore, the data-driven analytics on actual data and AIessence from multiple perspectives have not been covered in prior studies. To cover thesedeficiencies, this study provides insightful coverage of the state-of-the-art studies that havedevised ways to fight with COVID-19 pandemic leveraging AI. The main contributions ofthis article in the field of AI-based data-driven analytics in the COVID-era are summarizedas follows.
• It covers the role of AI in COVID-19-era in six distinct regards such as epidemiccontainment strategies (ECS), epidemic data life cycle (EDLC), epidemic handlingwith heterogeneous sources data (EHHSD), healthcare-specific AI (HCSAI) services,general epidemic AI services (GEAIS), and drug design and repurposing (DDAR)against COVID-19 that have not been covered in the recent literature.
• It discusses the challenges involved in applying AI on the available epidemic data thatis not in desirable form until present due to several problems (e.g., diverse formats,legislation, heterogeneous sources, and privacy concerns etc.).
• It elaborates the privacy issues that arise due to the person-specific data movement incyberspace amid the ongoing pandemic.
• It provides a concise overview of the latest technologies other than AI that contributedin the fight against the recent pandemic through their innovative features.
• It discusses many state-of-the-art studies that have applied AI techniques in theongoing COVID-19 pandemic for beneficence (i.e., greater good to save lives).
• It provides many state-of-the-art studies that have demonstrated the role of IoT basedon heterogeneous data stemming from the ongoing pandemic to lower its effects.
• It discusses the synergy of AI with other emerging technologies in order to lower theeffects of COVID-19 on the general public and economies.
• It provides the avenues of future research in the respective area keeping the latesttechnologies in loop.
The rest of this paper is organized as follows. Section 2 describes the prior researchstatus and compare presented work results with related work. Section 3 provides therole of AI in fighting against COVID-19 through unique services in six different aspects.Section 4 discusses the challenges involved in applying AI on the COVID-19 data that is notin perfect until present. Section 5 summarizes the work, discusses emerging technologiesrole, AI synergy with other techniques, IoT-based developments in COVID-19 context, andprovides promising future research directions. Finally, this article is concluded in Section 6.
2. Prior Research Status
In this section, we concisely present the contribution of previous studies, and compareproposed work results with related work. From the start of this deadly pandemic, AI hasplayed a vital role in tackling it from a non-pharmaceutical interventions (NPI) point ofview across the globe. The unique applications of the AI have paved the way to manage theresources well, and lowering the mortality rates through precise forecasting [19–21]. Withthe help of precise forecasting, extra care can be provided to the vulnerable people havingunderlying diseases, and treatment can be done on the regular basis [22,23]. Consequently,mortality rates and ICU admission can be prevented in most cases [24]. Arora et al. [25]discussed the potential applicability of AI in the development of early warning systemsand accurate and timely forecasting about cases/mortality leveraging social media data.The study suggested that AI can be used in different aspects pertaining to COVID-19, butvarious issues like unavailability of the large datasets, ethical concerns, security and privacy,and computing resources remain challenging. Huang et al. [26] provided comprehensive
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coverage of the AI in terms of clinical applications. For example, authors discussed thediagnosis of the COVID-19 via images, ultrasounds, chest scans, X-rays, lab indicators,electronic medical records, and lab indicators. Authors discussed AI role as experiencedphysicians for diagnosing COVID-19 robustly and accurately. We present the approach ofHuang et al. [26] that was proposed in order to perform diagnosis of leveraging AI basedon medical characteristics in Figure 3.
Figure 3. A flowchart of the AI methods employed for COVID-19 diagnosis and other relevantservices: ML and DL were mainly applied in the medical characteristic to diagnosis the COVID-19infection (Partially adapted from Huang et al. [26]).
Motta et al. [27] discussed the AI role from COVID-19 diagnosis and spread controlpoint of views. The authors stressed the need of AI-powered decision systems to fight withthe infectious diseases. Cave et al. [28] emphasized the need of using AI ethically whilefighting with the COVID-19. Authors discussed four pillars of the biomedical ethics inorder to get true benefits from the AI during crisis. The four pillars are (I) Beneficence, (II)Non-maleficence, (III) Autonomy, and (IV) justice. These pillars are an integral part of thehealthcare settings in order to truly benefit from AI applications.
• Beneficence: It means that AI use should be beneficent (i.e., save lives) in managingthe ongoing pandemic.
• Non-maleficence: It means the objective function of AI systems should be definedcarefully in order to avoid unintended harms while managing the pandemic. Forexample, imposing strict self-isolation on elderly people may lead to mental issues.
• Autonomy: It means that people should be autonomous while controlling and en-dorsing the technologies including AI during the pandemic. For example, diagnosticsupport systems employed by healthcare workers in a pandemic should provideenough information about the uncertainty surrounding, and assumptions behind, arecommendation, so that it can be included into their professional judgment.
• Justice: It means that when AI systems are devised for a response to a COVID-19-likepandemic, difficult trade-offs between values could be incorporated. For example,decision about whether to employ centralized or decentralized app approach for datacollection in order to manage pandemic should be based on justifiable grounds (e.g.,involvement of diverse communities/stakeholders in decisions).
Leslie et al. [29] discussed the dark sides of the AI in terms of health inequity. Au-thors suggest that in order to reduce the inequalities, a collaboration between differentstakeholders is paramount. An important perspective regarding the use of untested AIalgorithms/methods in COVID-19 context is presented by the authors [30]. Authors sug-gested that in order to fully affirm the role of AI in saviour of the pandemic or futurepandemics, we need to test the solutions with proofs. Chang et al. [31] concisely discussedthe role of the AI from different perspectives including diagnosis to therapy. Authors
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discussed the role of the AI in three aspects such as epidemiology (predictions mainly),diagnosis, and therapy. According to the study findings, mismatch between epidemiologyand data science need to be resolved in order to take advantage of the AI approaches forfuture endeavors. Vaishya et al. [32] analyzed the literature, and discussed the seven mostsignificant AI applications (as shown in Figure 4) for the COVID-19 pandemic. Authorssuggested that AI can play a dominant role in decision making and treatment consistencythrough robust algorithms. We fully agree with the contributions and significance of allstudies cited above in the context of COVID-19 pandemic.
Figure 4. Overview of seven innovative AI applications in the context of COVID-19 (Adapted fromVaishya et al. [32]).
The advantages and significance of the proposed work compared to the prior studiesare summarized as follows. (i) it provides insights about huge variety of data that isessential to fight with the COVID-19 leveraging AI from different perspectives, (ii) itdiscuss AI role in data life cycle and containment strategies that is not covered by anyof the previous studies from broader perspective, (iii) it discusses substantial number ofchallenges comprehensively that hinder the applicability of AI methods in the ongoingpandemic, (iv) it provides the coverage of AI applications from multiple perspectives ratherthan one/two aspects, (v) it discusses the role of other emerging technologies with whomAI can be converged to serve the mankind in an effective way compared to the recent past,and (vi) it highlights actual AI applications based on the real-world data originating fromthe COVID-19 diagnosis or clinical practices.
3. Role of Data-Driven Analytics Leveraging AI in the Era of Covid-19
This section concisely presents the role of the data-driven analytics leveraging AI in theera of COVID-19. We categorize the coverage of AI applications/services in six regards suchas epidemic containment strategies (ECS) that are in place as NPIs, epidemic data life cycle(EDLC) (aka data collection, storage, pre-processing, analysis, use, distribution, archival,and secure disposal) that is adopted in healthcare sector to fight with the infectious diseases,epidemic handling with heterogeneous sources data (EHHSD) as relying on a data from fewsources is insufficient to fight with COVID-19, healthcare-specific AI services (HCSAIS) thatcan reduce the burden of healthcare workers, general epidemic AI services (GEAIS), andAI role in drug design and repurposing against COVID-19. These services are unique andemphasize the effectiveness of AI in COVID-19 era. Our aim is to highlight the significanceof AI in COVID-19 context through relevant data and services. We identify data that relatewith COVID-19 through analysis of the COVID-19 characteristics, and corresponding data.For example, SN tweets and comments that use the word COVID or related aspects such asquarantine, social distance, amid pandemic, and spread etc. are classified as COVID-19data. In addition, some applications are specifically designed to collect and process datathat relate with COVID-19. In some cases, data is collected in a proactive manner, and itcan relate with COVID-19 when he/she tested positive. Furthermore, we mainly discuss
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the additional data collected to fight the COVID-19 that varies from site to site as shown inFigure 9. Apart from these services in six regards, AI has been widely used in analyzingthe vaccine distributions and other clinical aspects concerning COID-19. We describe eachperspective and AI services in each perspective as follows.
3.1. Perspective 1: Epidemic Containment Strategies and AI Role
From the beginning of the pandemic, each country of the world introduced certainstrategies to contain the spread of COVID-19, including strict lockdown, cities and schoolclosures, remote telehealth, closure of bars and clubs, and work from home, etc. Apart fromthese general containment strategies, many digital solutions based on the latest technologiesfor exposed people identification, close contact analysis, and compliance monitoring withthe disease guidelines were also developed. We call such solutions epidemic containmentstrategies (ECS), and provide different AI-supported services in each ECS. We presentthe role of AI in seven different ECS that were extensively used in COVID-19 in Figure 5.Based on the extensive review of published studies, we found that AI remained a criticalcomponent of every ECS developed to contain the spread of COVID-19 [33–37]. Apartfrom the services cited in Figure 5, AI can play a vital role in alerting people stay awayfrom the virus contaminated places pro-actively. In addition, it can be used to identify thefocus group to whom COVID-19 can affect more due to underlying diseases. Hence, therole of AI in each ECS is vital and essential. Data reported in Figure 5 can be gatheredfrom multitude of sources. For example, surveillance data can be employed for the contacttracing purposes [38]. We present an example of surveillance data based contact tracingexample in Figure 6.
Figure 5. Overview of AI-supported services in ECS in the context of COVID-19 pandemic.
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Figure 6. Example of surveillance data based contact tracing for COVID-19 suspects finding. (1)Person A goes to work, bringing a Bluetooth-enabled cell phone with a digital key, which is used tocommunicate with other cell phones. (2) Person A comes in close contact with persons B, C, and D;all their cell phones exchange key codes with each other. (3) Person A later learns he is infected withCOVID-19 and enters his updated status in the app. (4) By agreeing to share his recent status withthe database, A instructs the app to send the data to the cloud service. (5) Meanwhile, B’s, C’s, andD’s phones are regularly synchronising the cloud database to check the status of their users’ closecontacts. When B, C, and D discover that person A has reported himself infected, they all know theyshould get tested for the COVID-19 (Adapted from Hsu et al. [39]).
The mobile devices generated big data that can be used to monitor the people underquarantine [40]. In addition, GPS data can also be gathered for quarantine monitoringpurposes [41]. In South Korea, health authorities usually call people on their cell-phonerandomly, and such calls data can be used for the quarantine monitoring. The data tomonitor social distance can be collected through Bluetooth technologies, video sequences,smart camera, and IoT platforms [42–45]. Data about the exposed/infected people canbe gathered from the relevant clinics/diagnostic-centers. It can be shared with differententities to find the contacts of infected people [46]. COVID-19 symptoms and other relateddata can be gathered with the help of the wearable sensors, ambient tools, and smartphone technology [47,48]. Personal data at the time of the check-ups can be collected viaautomated/traditional forms. Subsequently, this data is used to rank the areas based onCOVID-19 prevalence etc. Finally, CT and X-ray images data [49], ultrasound imagingdata [50], text data [51], social media data [52], biomedical data [53], and big data [54] can beused during analytics. Apart from the data sources mentioned above for each ECS, data canbe collected from heterogeneous sources for each category. For instance, analytics can beperformed on data collected from variety of sources such as smart watches, sensors, mobiletechnology, CCTV cameras, GPS locations, Bluetooth devices, and smart write bands, toname a few. In Figure 5, the risk indexes are the quantitative values that denote the riskof being infected with COVID-19 based on gender, age, health status, city of residence,and knowledge of vaccine/treatment. This value is highly useful to evaluate infection riskaccurately and taking preventive measures accordingly. AI can be used to rank the mostinfluential indicators, and predicting the index accurately. The parameters used in each AI
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model can be different. For example, if random forest is employed then parameters canbe number of trees, variables required for tree split, sampling scheme, trees’ complexity,and model type, etc. The choice of parameters and their values highly depends on the AImodel chosen for the desired task.
3.2. Perspective 2: Epidemic Data Life Cycle and AI Role
In South Korea, if a person tests positive for COVID-19, then his/her contact detailsare collected to find the exposed people [55]. Different entities (i.e., police, mobile carriers,and credit card companies, etc.) collect contact data and process it in accordance with thespecified procedures. This mechanism usually follows a lifecycle such as data collection,storage, pre-processing, analytics, use, archival, and deletion. We call this whole process anepidemic data life cycle (EDLC). We describe the role of AI in the EDLC in Figure 7.
Figure 7. Overview of AI-supported services in EDLC in the COVID-19 context.
AI can enable real-time decision making based on the collected data through EDLC.Since EDLC is essential to curb the spread of COVID-19, different AI mechanisms can beused at each phase of the EDLC. Although AI contributes significantly in all phases of theEDLC, geo-fencing of a certain area to contain the spread of COVID is one of the mostuseful applications. In geo-fencing, people of certain areas are put under home quarantine,and later they are monitored whether they are within the geo-fenced area or not usingAI [56,57]. The example of a geo-fenced area (also referred as hotspot) with a dotted line isshown in Figure 8.
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Figure 8. Example of the geo-fenced area.
In South Korea, health authorities usually keep track of the geo-fenced zones/areasand people living in respective areas through real-time location data processing via smartphone application. Furthermore, wrist bands were also used to monitor the quarantineviolations in the geo-fenced areas. Data reported in Figure 7 can be collected from theinfected individuals by either interviewing them or using the devices (i.e., cell phones)owned by them. Furthermore, every country has implemented various epidemic handlingsystems in the form of platforms, mobile apps, and integrated frameworks for supportingthe EDLC. For example, South Korea has implemented an epidemic investigation supportsystem (EISS) in which data from multiple companies (e.g., credit card, mobile carrier, andpharmacies etc.) is fed into it [58]. The EISS has an ability to collect and share the data withrelevant and authorized entities. Similarly, Singapore Government asked their citizens toinstall a mobile app through which social interactions were recorded and processed withproper consents. Furthermore, data in EDLC can also be collected from the IoT devices anddigital tools such as CCTV. The CCTV data have played a vital role in finding the COVID-19suspects in South Korea. For example, when a sporadic cluster emerged at a gay club,then CCTV data has played a vital role to identify the people who may have been comeinto contact with infected people leveraging multiple CCTVs footage. We invite interestedreaders to gain more insight about type and nature of data processed in EDLC in previousstudies [59,60]. In [59], authors discussed the consequences of COVID-19 on differentpeople based on their working environments. In [60], authors discussed the importance ofbig data technologies in processing large scale data. Most of the AI models can handle themissing values present in a data. Furthermore, a simple and popular approach to addressmissing values related issues is data imputation. It employs statistical methods in order toestimate a value for a column from those values that are present, then replaces all missingvalues in the column with the calculated statistic. Furthermore, many AI models predictthe missing values based on the original data statistics, and determine the missing values.
3.3. Perspective 3: Epidemic Handling with Heterogeneous Sources Data and AI Role
Due to the nature of this pandemic, reliance on single data source, for example,relying solely on individual memory to figure out the contacts he/she has made in thepast fourteen days or using popular SN data only to find vulnerable regions [25], etc.) hasproven unsatisfactory in many countries across the globe. For example, in South Korea,manual epidemiological investigation (i.e., interviewing confirm patients about their travelinformation, facilities visits, and persons to whom they met etc.) has failed badly (inaddition, it can slow down the containment of virus due to reliance on someone’s memoryand inaccuracies), and in addition to manual investigation, heterogeneous sources datacollection and analysis helped to keep daily cases at a manageable level [61,62]. By usingdata acquired from different sources such as cellular network, credit card, surveillancecameras, and facilities-visits logs to find the exposed people is called epidemic handling
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with heterogeneous sources data. Based on the extensive analysis of literature and technicaldevelopments [58,63], we classify the countries based on amount of heterogeneous sourcesdata used to handle COVID-19 in four different categories in Figure 9. From Figure 9, itcan be observed that South Korea used a huge amount and variety of data. Consequently,South Korea has better control on the ongoing pandemic without strict lock-down. Incontrast, France has used less data, but the use of some apps was mandatory, therefore,heterogeneity in data was high. Middle eastern countries have primarily focused onmonitoring people compliance rather than huge data collection. In Pakistan, the adoptionof digital mechanisms in healthcare industry is relatively low, which is why only requireddata (e.g., voluntary reporting) was collected and processed during the ongoing pandemic.The heterogeneity in collected data in middle eastern countries and Pakistan are averageand low, respectively.
Figure 9. Overview of heterogeneous data collected in different countries of the world.
The different data types listed in Figure 9 can be collected through combination of thedigital and manual methods. For example, geolocation data can be collected using GPSsensors or Bluetooth devices. In South Korea, geolocation data of the confirmed patientswas collected through mobile carriers. Symptoms and quarantine monitoring relateddata can be collected using low-cost sensors and/or calling people at random times andacquiring location in real time. Personal data can be collected through forms and websites,etc. Facility visit data can be obtained from logs maintained by each organization on dailybasis, and historical diseases data can be obtained from hospital databases and mobile-phone based surveys. Furthermore, travel data can be obtained from the travel agenciesor airport staff. Recently, unmanned aerial vehicles have also been deployed to monitorpeople’s compliance with the government guidelines. In addition, data is mostly collectedprior to taking COVID test in South Korea. Furthermore, interviews and surveys arepromising tools to acquire data. In some countries, cough sound, breathing patterns, bloodsamples, and temperature reading were taken through integrated platforms and sensors.Furthermore, to assess the spread, weather agencies and meteorologists also contributedto data collection. SN data have also paved the ways for generating symptoms taxonomy,and identification of new and related symptoms to COVID-19. Plenty of data collectionmethods have been comprehensively discussed by Hensen et al. [64]. We demonstrate theanalytics to be performed on the heterogeneous data sources using AI in Figure 10. Theanalytics results can be extremely useful to keep the cases at a manageable level in orderto lower the healthcare burden. Using data-driven analytics from different context canalleviate the pandemic’s crisis.
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Figure 10. Overview of AI-supported analytics on heterogeneous sources data.
Data reported in Figure 10 can be collected from a variety of devices, apps, andsearch engines, to name a few. IoT data can be collected from wearable sensors [65],contents, and other related data can be gathered from e-commerce websites [66], mobiledata can be gathered from mobile carriers/service-providers, social media data can becollected from SN service providers [67], historical data can be gathered from the hospitalswebsites/repositories [68], medical images and sounds data can be collected with wearabledevices or automated machines, and demographics data can be obtained from trustedclinics/hospitals [69]. The logistic regression based models can assist in identifying hotspotsin any territory based on the certain environment parameters and underlying conditions[70].
3.4. Perspective 4: Healthcare-Related Services and AI Role
Besides AI use in data analytics, ECS, and EDLC described earlier, AI can be highlyuseful to assist in carrying out diagnosis and trend analysis to assist mankind in an ef-fective way [71–73]. In this perspective, we discuss the possible use of AI in healthcare-related services that can have a direct impact on the virus virulence in any country. Theseservices have unique utilities such as lowering hospital burdens [74], caring for elderlypatients [75], separating the more risky people [76], and preventing people from beinginfected with COVID-19 [77]. To this end, we describe many healthcare-specific services ofAI in Figure 11. We invite interested readers to the previous study for more details abouteach service in the context of ongoing pandemic [78].
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Figure 11. Overview of AI-supported services in healthcare in COVID-19 context.
3.5. Perspective 5: General Epidemic Services and AI Role
In this perspective, we shed light on AI use in other sectors that were directly impactedby the COVID-19 pandemic. For example, predictions about when aviation industriescan return to normal [79], how much transportation use reduced in each country due toCOVID-19 [80], and recommendations of show/music to alleviate people’s stress duringthis pandemic [81]. AI can play a vital role to access the risk and challenges of any sectorduring these unprecedented and unanticipated times [82]. In some sense, AI is loweringthe human involvement in many sectors through automated and real-time decision makingabilities [83]. For example, in south Korea, AI-powered robots were installed at the airportsthat were carrying similar tasks (i.e., temperature checking, mask status analysis andalerting, social distance monitoring and alerting in case of breaches, and preventing clusterformation of people at one place etc) as humans do [84]. In the new normal, AI use canpossibly increase in many diverse sectors. For example, performing analytics using AIbased on spatial-temporal data can be handy to predict and prevent future pandemics [85].In addition, AI experiences can be applied to other epidemics to fight them effectively. Wepresent general epidemic related services of AI in Figure 12.
AI uses in all six perspectives lay a solid foundation for future studies in the samearea. It enables researcher and developers to understand the applicability of AI in differentcontexts. Furthermore, it provides conceptual foundations of AI use in different aspectsrelated to pandemic and AI role in each aspect. Furthermore, these concepts can beexploited to devise new techniques for each services. In addition, there is a chance ofimprovising each AI technique on COVID-19 data that is relatively new and requires amplework to make sense of it.
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Figure 12. Overview of AI-supported general services in COVID-19 context.
3.6. Perspective 6: AI Role in Drug Design and Repurposing against COVID-19
Besides the AI use in the five different perspectives cited above, AI/ML has also beenextensively used in computer-aided drug design and repurposing existing drugs againstCOVID-19 receptor proteins [86,87]. Monteleone et al. [88] discussed the role of AI in drugrepurposing with therapeutics analysis for treating infected individuals with COVID-19. Inthis regard, we summarize the findings of recent SOTA studies in Table 1.
Table 1. Summary of the AI uses/applications in designing drugs and repurposing existing drugsagainst COVID-19.
Ref.Discussions about AI Use in the Era of COVID-19 along with Models Details and Purpose Achieved in the Context of COVID-19.
AI Technique Used Purpose in the Context of Designing Drugs and Repurposing Existing Drugs
Zhou et al. [89] Fully connected feedforward neural network (FNN) Drug repurposing for precision medicine and personalised treatment
Walters et al. [90] Quantitative structure–activity relationships (QSARs) Drug discovery by predicting the physical properties and biological activity ofmolecules
Patronov et al. [91] Deep neural networks (DNN) AI-based generative models for drug design to combat the COVID-19
Arora et al. [92] Deep neural networks (DNN) Protein synthesis, molecular changes, time management in laboratory for drug dis-covery
Bhati et al. [93] ML integrated with PB Sampling of relevant chemical space for target proteins analysis to make pandemicdrugs
Kabra et al. [94] Combined AI approaches Finding possible drug candidate to treat COVID-19 patients with antiviral drugBai et al. [95] Genetic algorithm 3D drug design of protein targets for treating COVID-19 patientsLiu et al. [96] Graph convolutional network (GCN) Drug repositioning framework to quickly identity potential drugs for COVID-19Delijewski et al. [97] Gradient boosting tree (GBT) Identification of zafirlukast as one of the repurposing candidates for COVID-19Haneczok et al. [98] Graph-CNN Prediction of molecular property and identification of SARS-CoV-2 3CLpro inhibitors
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4. Data Challenges While Applying Ai in the Era of Covid-19
AI has played a vital role in many aspects to curb this disease spread. Meanwhile, thetrue applications were hindered by data that is not perfect and complete in many regards.We present a taxonomy of the challenges related to data that can possibly hinder AI use inFigure 13. These challenges need to be resolved to truly benefit from AI techniques. Someof these challenges can be solved by AI itself. For example, multi-model mechanisms can beused to make sense of heterogeneous sources data, feature engineering can be employed tofilter redundant/less-important data before applying AI, data manipulation can be reducedby using advanced form of AI (i.e., federated learning), synthetic data can be generatedby projecting original data using AI, and dimensionality can be reduced using many AItechniques.
Figure 13. Challenges involved in applying AI in the COVID-19 era due to data issues.
Concise description about each challenge, indicators, and possible solutions are de-scribed as follows.
• Heterogeneity of data styles: During this pandemic, the data of diverse types is beingcollected. For example, in South Korea, when a person is confirmed to have a COVID-19, his/her data (contacts, place, demographics, and 14 days visits to every place, etc.)is collected in heterogeneous formats. For example, the routes information can be ingraph form, buying items can be in tabular form, and facilities he/she has visited canbe in matrix form. Hence, fusing this heterogeneous data from different contexts tofind the potentially exposed people is very challenging. It requires parsers and unifiedformat conversion to deal with diverse data that is very challenging.
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• Heterogeneous sources data handling: During this pandemic, the data can originate fromdifferent sources. For example, in South Korea, fine-grained and sufficiently detaileddata is collected to curb the disease spread. For example, healthcare sectors areconstantly acquiring data from law and enforcement agencies, credit card companies,and SN etc. Hence, handling this heterogeneous sources data during pandemic timeis very challenging. It requires interfacing and transparent policies and data-drivenapproaches to address this challenge.
• Data manipulation and misuse: During this pandemic, a huge amount of personal datais being collected on a daily basis. For example, mobility data, trajectory information,buying data, and social interactions, to name a few. Hence, it increases the chance ofmanipulation and misuse. It requires legal, organizational, and technical measures toaddress this challenge.
• Data volume: During this pandemic, a huge amount of data is being collected on a dailybasis about people. For example, in South Korea, before entering any facility, data isbeing collected along with explicit identity information. Similarly, cellular networksdata is used to perform crowd analysis, and identifying people at controversial places.Hence, handling of such higher volume of data is challenging, and it requires usage ofhigh performance computing model. Additionally, it requires low-cost reduction andcompression techniques to address this challenge.
• Lack of data knowledge: During this pandemic, a huge amount and diverse typesof data is being collected from heterogeneous mediums. AI experts and analyticscompanies have limited knowledge of data structures and formats. For example,temporal and spatial data can be in different formats and styles. Hence, convertingsuch a data into consistent styles prior to AI application is very challenging. It requiresdomain expertise and visualizations techniques to address this challenge emerging inpandemic times.
• Data convergence issues: In this pandemic, data was being collected from different sources,and in different styles (e.g., graphs, matrix, tables, etc.). Correlating/converging dif-ferent subjects data gathered from different sources and styles is challenging. Forinstance, analyzing the characteristics of each subject based on data he/she producesor consumes using different sources is an extremely difficult task. It requires pre-processing and similarities-based approaches to address this challenge emerging inpandemic times.
• Inadequacy of metrics: In this pandemic, the majority of data analytics was performedusing existing metrics that yields imprecise results considering the huge dynamics ofCOVID-19. For instance, analyzing disease spread based on daily cases and ambientconditions is difficult in the absence of desired metrics. It requires new metrics oramendments in the existing metrics to make them more suitable for use.
• Lack of truthful data: In this pandemic, huge variations were observed in each territoryregarding the disease severity, symptoms combination, and virus effect on the differentage groups. For example, disease characteristics observed in South Korea exhibit largedifferences to those observed in Japan. Hence, to clearly understand the diseasedynamics, there is a lack of truthful data, although some companies/researchersgenerated synthetic data that is close to the original data for understanding/modelingof the COVID-19 dynamics. Moreover, synthetic data may yield imprecise results inthe absence of evidence-based truthful data [99]. This challenge can possibly be solvedthrough data sharing with domestic and international firms, and analyzing data withadvanced AI techniques.
• Mishandling in finding exposure of contacts: To accurately find the contacts of an infectedperson, close monitoring of all subjects in outdoor environments is paramount. Forexample, it requires monitoring of who met with whom? for how long he/she met?what was the nature of contact (e.g., had dinner/lunch or just crossed), whether he/shewas wearing masks perfectly or not? and how often he/she met with each other. Tocapture and analyze all these aspects with fine-grained data collection about eachsubject can be highly difficult, and it can lead to hidden/silent transmission of COVID-
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19. This challenge can possibly be solved through data collection from heterogeneoussources, and analyzing data with advanced AI techniques and integrated platforms.
• Data collection in a fine-grained manner: Due to the nature of this pandemic (i.e., spreadthrough close contact), data about people should be collected in a fine-grained manner.Meanwhile, in some countries, data protection laws are in place, therefore, fine-graineddata collection is not allowed. Due to which virus can spread, containment is not easyat all. For instance, the recent pandemic spread at a wider scale in European nationsdue to general data protection regulation (GDPR), which restricts data collection aboutsubjects without their explicit permission. This challenge can possibly be solvedthrough amendments in laws considering the severity of the virus for public safety.
5. Discussion on AI and Latest Technologies Use and Future Research Directions
In this section, we concisely discuss AI use/services in COVID-19 context based onactual data and purpose of using AI, latest technologies that have been used in the eraof COVID-19, synergy of AI with other emerging technologies, and promising researchdirections for future endeavours.
5.1. Discussion on AI Use in the Context of COVID-19
So far, we have reported the coverage of AI-based analytics in the COVID-19 era fromsix perspectives such as ECS, EDLC, EHHSD, HCSAIS, GEAIS, and AI role in drug designand repurposing against COVID-19. We have rigorously and thoroughly analyzed thescope of AI in all six aspects. This concise overview will enable future research in thisarea with clear directions/gaps. Specifically, we highlighted the data related challengesthat need to be resolved to yield higher adoption of AI, as AI has already demonstratedeffectiveness in many aspects related to the COVID-19 [100–105]. Thus, we summarizeAI use based on actual data used/processed, AI models applied, and purpose/serviceachieved in COVID-19 context in Figure 14.
Figure 14. Practical uses of AI to fight with the ongoing pandemic.
Apart from AI unique services cited above, they can be extremely useful in preventingdiseases by predicting high risk facilities, and identification of lethal combination based on
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geographic data that can lead to a higher number of deaths. Furthermore, AI can be handyto analyze protein sequences that can assist in developing potential vaccines for futurepandemics. Furthermore, many unique aspects such as robustness, efficiency, large datahandling, and reduction in time and cost make AI more attractive for many applicationsin the healthcare sector. In many countries, AI has been integrated as a main modulewith decision support systems (DSS) to efficiently fight this pandemic. Apart from the AIservices explained above, we describe various promising applications of the AI in the eraof COVID-19, keeping AI techniques used in loop in Table 2.
Table 2. Comprehensive overview of the AI use/applications in the era of COVID-19 discussed inrecent SOTA studies.
Ref.Discussions about AI Use in the Era of COVID-19 along with Models Details and Goals Achieved in the Context of COVID-19.
AI Technique Used Purpose in the Context of COVID-19 Pandemic
Pinter et al. [106] Multi-layered perceptron Predictions of mortality rate and infected casesAminu et al. [107] Deep neural networks Detection of people with COVID-19Magar et al. [108] Ensemble techniques Virus–antibody sequence analysis and patients’ IdentificationZeng et al. [109] Extreme Gradient Boosting (XGBoost) Forecasting of patient survival probabilityAshraf et al. [110] Machine & deep learning models Predict the severity of disease or chances of deathShah et al. [111] Convolutional neural network (CNN) COVID-19 detection from X-ray imagesPrakash et al. [112] Autoregressive Integrated Moving Average Impact analysis of various policiesRathod et al. [113] AI Prediction models Effective crisis preparedness and managementUllah et al. [114] Logistic Regression and Support Vector Machine Classification of patients with/without COVID-19Rathod et al. [115] SVM, RProp, and Decision tree Detection of abnormal data for effective analysisHu et al. [116] Spectral Clustering (SC) algorithm Feasible analysis model for the treatment & diagnosisRashed et al. [117] Long short-term memory (LSTM) network Provides public awareness about the risks of COVID-19Singh et al. [118] ResNet152V2 and VGG16 CNN Reduce the high false-negative results of the RT-PCRSaverino et al. [119] Digital and artificial intelligence platform (DAIP) Changes implementation in rehabilitation servicesPeddinti et al. [120] Convolutional Neural Network (CNN) Detection of COVID-19 cases in public placesMalla et al. [121] Ensemble deep learning model Real-time sentiment analysis of COVID-19 dataLella et al. [122] Convolutional Neural Network (CNN) model Respiratory sound classification for patient identificationHaleem et al. [123] Artificial neuronal networks (ANN) Predictions of survival of COVID-19 patientsHashimi et al. [124] Deep learning models Tracking and identifying potential virus spreadersAmaral et al. [125] Artificial neuronal networks (ANN) forecasting and monitoring the progress of Covid-19Zgheib et al. [126] Collection of ensemble learning methods Detecting COVID-19 virus based on patient’s demographicsFerrari et al. [127] Bayesian framework Predictions about the behavior of the COVID-19 epidemicAlmalki et al. [128] COVID Inception-ResNet model (CoVIRNet) Automatic diagnosis of the COVID-19 patientsUmair et al. [129] VGG16, DenseNet-121, ResNet-50, and MobileNet diagnosis of the virus at early stages via X-rays and transfer learningTamagusko et al. [130] EpiEstim framework Analysis of the population’s mobility during the COVID-19 pandemicArvanitis et al. [131] Ensemble learning methods (RF, SVM, and ANN) short-term and accurate prediction of effective reproduction number (Rt)Hussain et al. [132] Ensemble learning methods (RF, SVM, and ANN) Analysis of public attitudes on Twitter & Facebook toward COVID-19 vaccinesKumari et al. [133] Combination of multi class SVM and CNN models Contact less authentication system and face mask identificationTalahua et al. [134] OpenCv’s face detector and MobileNetV2 architecture identifying whether people are wearing face masks or notYu et al. [135] GCNN ResGNet-C under ResGNet framework Effective diagnosis of COVID-19 from lung CT imagesNayak et al. [136] Lightweight and robust CNN scheme Faster and accurate diagnostics of COVID-19 patientsBekhet et al. [137] Lightweight CNN architecture Recognizing COVID-19 patients with a 96% accuracy
Keicher et al. [138] Lightweight clustering method Patients outcomes prediction admission to ICU, need for ventilation and mor-tality
Alshazly et al. [139] Deep network architectures and transfer learning strategy CT images-based diagnosis of COVID-19 infected people in an automated wayCarvalho et al. [140] Convolutional features & genetic algorithms Screening and diagnosis of COVID-19 patientsFu et al. [141] Lightweight DenseANet architecture Distinction between pneumonia and COVID-19 patients using CT imagesBougourzi et al. [142] Pre-trained XG-boost classifier Analysis of sensitivity of the COVID-19 patients from CT images dataSong et al. [143] Details relation extraction neural network (DRENet) Person-level diagnoses of COVID-19 using CT imagesAlruwaili et al. [144] Inception-ResNetV2 deep learning model Visualization of the lungs’ infected regions using CXR images
Wang et al. [145] Inception transfer-learning model Extraction of radiological features for timely and accurate diagnosis of COVID-19
Jha et al. [146] logistic regression, SVM, Random Forest, and QSAR Robust drugs discovery and extraction of features combating COVID-19Abbas et al. [147] DeTraC deep convolutional neural network architecture Classification of COVID-19 chest X-ray images
Sedik et al. [148] CNN & convolutional long short-term memory (ConvL-STM) AI-powered COVID-19 detection system using X-ray and CT data
Bhardwaj et al. [149] Inceptionv3, DenseNet121, Xception, and InceptionRes-Netv2 Quick and highly accurate automated COVID-19 detection
Muneer et al. [150] Deep hybrid NN models (GCN-GRU and GCN-CNN) Prediction of RNA degradation from RNA sequencesAli et al. [151] Keras Classification model (also called Keras classifier) Classifying COVID-19 spike sequences from geographic locationAhsan et al. [152] Histogram-oriented gradient (HOG) and CNN Detect of COVID-19 from the chest X-ray images using model fusionRaji et al. [153] Convolution Neural Networks using medical modalities Robust detection of the virus by using the pre-trained modelsTeli et al. [154] shallow and simple CNN-based approach, named TeliNet Robust classification of CT-scan images of COVID-19 patientsJacobs et al. [155] Generative deep learning models Small molecule drug design using scalable deep learning for COVID-19Madhavan et al. [156] Res-CovNet: A hybrid methodology Classification of multiple diseases using X-ray imagesShorfuz et al. [157] IoT-enabled deep learning-based stacking model Analysis of chest CT scans for diagnosis of COVID-19 encountersShankar et al. [158] Cascaded recurrent neural network (CRNN) model Detection and classification of the existence of COVID-19Saranya et al. [159] Recurrent NN utilized the TensorFlow Keras framework COVID-19 mortality prediction using electronic health recordsAlhudhaif et al. [160] CNN model built on DenseNet-201 architecture Determination of COVID-19 pneumonia from X-ray imagesAboutalebi et al. [161] COVID-Net CXR-S, a convolutional neural network Predicting the airspace severity of a COVID-19 positive patientsZhao et al. [162] convolutional neural network (CNN) COVID-19 identification from a small subset of training data
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5.2. Discussion on Latest Technologies Used to Fight with COVID-19 Pandemic
Beside AI, numerous other latest technologies such as blockchain (BC), federatedlearning (FL), few short learning (FSL), robotics, and confidential computing (CC) havealso played a vital role in this pandemic. For example, BC technology has promisingcharacteristics such as transparency, immutability, verifiability, and privacy-preservationwhich makes it suitable for data sharing securely between different entities [163]. Inaddition, it can be used to monitor the personal data flows in the healthcare environments.FL is an emerging technology in which data in not shared, instead model results are sharedonly. It can be highly beneficial to ensure individual privacy. Furthermore, it enablesmodel results sharing at a wider scale without privacy disclosures [164]. FSL enablemachine learning model’s training from a few data samples. It has a lot of applications inanalyzing the dynamics of COVID-19 by extracting knowledge using a limited data [165].Robotics were used to deliver the test samples from testing sites to hospitals, and manyother innovative applications [166]. In addition, they were also used to check people’stemperature in the streets. Some countries used robotics to monitor people’s mobilityduring the rush hours. CC techniques were employed to secure the personal data since itcan use the data without accessing actual values. All these technologies have played a vitalrole in serving mankind during this deadly pandemic. We summarize the role of the sevenlatest technologies that were used in this pandemic as follows.
• Blockchain (BC): The BC technology has been widely used in addressing the challengesof privacy in healthcare sectors [167,168]. The unique capabilities of the BC such asdecentralization, transparency, immutability, and traceability makes it useful foralleviating privacy problems of data storing, distribution, and utilization phases. BChas been rigorously used in this pandemic for transparency and verifiability relatedpurposes [169].
• Decision Support Systems (DSS): DSS can play a vital role in lowering the burden ofthe medical staff [170]. They can be extremely useful for the ETL (extract, transform,and load) purposes and performing the desired screening tasks in an automated ways.DSS can be very helpful in lowering the burden of healthcare workers and planningresources accordingly.
• Explainable AI: It is very recent technology with a wide range of practical applicationsin the diagnosis and analytics [171]. It can be extremely useful in identifying thehidden routes of disease transmission and vulnerable communities analysis.
• Internet of things (IoT): IoT has revolutionized the medical sector with unique abilitiesof remote monitoring, connected healthcare, and constant tracking [172]. IoT canbe highly useful in symptoms reporting, remote analysis of patients, and patientsmonitoring in ICUs, etc.
• Confidential computing and zero knowledge proofs: Both these techniques havehigher utility in data distribution with different stakeholders [173]. These techniquesenable data utilization with higher privacy guarantees. These solutions can be widelyacceptable to address the privacy implications of the data distribution with domesticand international researchers.
• Natural language processing: It can be highly useful in the analytics phase of theEHS for symptoms extraction and sentiment analysis [174]. It can also be useful forsymptoms clustering and forming a unified taxonomies of epidemic diseases.
• Search Engines (SE): The SE has played a vital role in devising the common symptomsof the infectious diseases, and it can assist in identifying the origin of pandemics. Thetools can be employed to recommend helpful tips to the people to reduce the chaoscreated by pandemic.
In order to fight COVID-19, every country has implemented digital solutions andlaunched many projects. For instance, South Korea has implemented an integrated plat-form named epidemic investigation support system (EISS), in which data about infectedpatients is collected and shared with relevant agencies [61]. Furthermore, South Koreahas implemented many smartphone apps for contact tracing, quarantine monitoring, and
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logging with the help of different ministries [175]. China has used plenty of AI techniquesand DSS in response to COVID-19 [176]. Pakistan has launched a web-based portal toreport the statistics of virus, and multi-criteria based data-driven testing strategy [177].Singapore has implemented an app for social interactions recording that can be used laterif someone get infected with COVID-19 [178]. Some countries have used AI and drones etc.to curb the spread of COVID-19. In the U.S., national COVID cohort collaborative (N3C)project is being launched in which multiple organizations are collaborating on clinicaldata related to COVID-19. In addition, the N3C project aims to answer important researchquestions that in return will assist to combat the COVID-19 pandemic [179]. Many countrieshave developed a variety of digital solutions during this pandemic to keep their citizenssafe [180–183].
IoT and various smart sensing technologies are also playing a key role in the pandemicarena, leveraging COVID-19 pandemic related data for various purposes such as remotemonitoring, quarantine management, and symptoms reporting [184–186]. A number ofsurvey studies have discussed the IOT role in the ongoing pandemic [187,188]. In thisregard, we summarize the role of IoT in COVID-19 pandemic reported by the SOTA andrecent studies in Table 3.
Table 3. Summary of IoT and various smart sensing technologies role in fight against the COVID-19pandemic.
Ref.Discussions about AI Use in the Era of COVID-19 along with Models Details and Purpose Achieved in the Context of COVID-19.
Data Sources Purpose Achieved in the Context of Lowering the Effects of COVID-19 on General Public
Sharma et al. [189] Wearable sensors Timely and accurately predicting COVID-19 positive cases to control the spread of COVID-19 pandemicKhan et al. [190] Camera sensors Monitoring and countering the spread of ongoing pandemic using IoT sensors data in close indoor spaces
Awotunde et al. [191] Sensing technologies Advising patients about their health conditions preventive measures suggestions to saving lives amid the pan-demic
Abdulkareem et al. [192] Medical devices AI and IoT based clinical decision support systems for COVID-19 pandemic handling in smart hospitalsJayachitra et al. [193] Handheld devices IoT-based cognitive system with 100% prediction accuracy of COVID-19 infection using multimodal dataHerath et al. [194] Thermal cameras IoT-based system to detect & control the ongoing pandemic inside the hospital environmentMukherjee et al. [195] Medical devices IoT-cloud-based healthcare predictive model in order to quickly detect COVID-19 using eKNNAkbarzadeh et al. [196] Wearable sensors Notifying end-users when breaking the social distance guidelines in the situation of COVID-19 pandemic
Petrovic et al. [197] Sound sensors A cost effective IoT-based practical solution for reducing the spread of COVID19 in indoor settings using coughsounds
Alamri et al. [198] IoT sensors Providing real-time information to the users about potential events that can affect the public transport in COVID-19 times
Poongodi et al. [199] COVID-specific sensors A robust health-based fully connected IoT systems in order to strengthen full COVID-19 administration usinglocation data
Kent et al. [200] Mobile sensors IoT-based solution for hospitals in order to improve health monitoring and providing timelier healthcare for pa-tients
Krishnan et al. [201] Multiple sensors Checking the availability of the mask in initial stage and monitoring the students’ temperature in the latter stageMylonas et al. [202] IoT sensors Analyzed the effects of COVID-19 pandemic on a multiple schools in Greece for monitoring energy and noisesBhowmick et al. [203] IoT sensors Process and help us monitor the health of older people in clouds based on different medical IoT sensors dataHerath et al. [204] IoT sensors Prevention of the COVID-19 pandemic using IoT-based platform in a smart city environmentsHerath et al. [205] IoT sensors Monitoring the symptoms of COVID-19 infected patients, and detecting the patient’s activities using mobile appLastovicka et al. [206] IR and ultrasound Contactless solution for automatic induction of disinfection intelligent hand sanitizer to lower spread of COVID-19Rajasekar et al. [207] RFID tags IoT-based automated tracking and tracing method for identification of the possible contacts of COVID-19 patients
Alhmiedat et al. [208] Wearable sensors Slowing the spread of COVID-19 locally and across the country by allowing individuals to maintain social dis-tances with others
5.3. Synergy of AI with other Emerging Technologies in the Context of COVID-19
In recent years, AI has been increasingly used in combination with emerging technolo-gies such as IoT, IoMT, cloud computing, fog/edge computing, and federated analytics,to name a few for accomplishing multiple goals [209,210]. Ahuja et al. [211] discussed AIuse from three different perspectives such as drug discovery, public communication, andintegrative medicine in the context of COVID-19. Márquez et al. [212] discussed the jointuse of AI and big data in the era of COVID-19. According to the authors, these synergiesbetween disruptive technologies can facilitate obtaining relevant data that, in return, ishelpful for health-related decision-making. Anjum et al. [213] discussed the emergingtechnologies to fight the COVID-19 pandemic. The authors have discussed the role ofAI and IoT-assisted drone technology, these synergies between emerging technologiescan pave the way to fight future infectious diseases using technology. Ahmad et al. [214]discussed the role of AI in COVID-19 pandemic by performing analytics on data stemmingfrom COVID-19 pandemic. The authors discussed the synergies between various emerging
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technologies for healthcare decision support leveraging IoT sensors. Lainjo et al. [215]discussed the big data and AI synergies that have helped various nations in improvingthe pandemic situations and reducing the adverse impacts of COVID-19 on economies.Bazel et al. [216] discussed the synergies between AI and three other emerging technologies(i.e., IoT, blockchain, and big data technologies) in healthcare in order to prevent the spreadof COVID-19. Swayamsiddha et al. [217] presented a comprehensive analysis of AI-aideddetection of COVID-19 using heterogeneous sources of data (i.e., AI-enabled imaging).Deshpande et al. [218] discussed the promising applications of AI leveraging audio signalsdata. The authors have demonstrated the diagnosis and screening of COVID-19 patientsusing audio-based analysis. Despite these promising applications, AI is a promising so-lution for supply chain management in the post COVID-19 era [219]. A comprehensivediscussion on synergy between blockchain and AI, along with their benefits and limitations,has been reported in the recent literature [220]. Recently, Deepti et al. [221] demonstratedthe gamut of synergistic applications including AI, cloud-enabled IoT, connected sensorsand actuators, and ubiquitous Internet to form connected communities that, in return,can help to fight the COVID-19 pandemic [221]. Furthermore, AI is an important pillarof industry 5.0 [222,223]. A relatively new concept, artificial intelligence of things (AIoT),has emerged as a new concept for addressing the potential limitations of IoT in health-care 4.0 [224]. Considering the promising applications of AI in the healthcare sector, itssynergy with other technologies is likely to increase in the near future for accomplishingmultiple goals. Hence, it has become an emerging avenue of research in recent years toserve the mankind effectively.
For the convenience of readers, we provide the summary of the important AI-relateddevelopments (adapted from [225]) reported in the published surveys in Figure 15. Theanalysis presented in Figure 15 shows that AI has contributed significantly to multipleareas (e.g., drug design and development, diagnosis, and surveillance, to name a few). Onthe other hand, this analysis can enable researchers to contribute more to areas that havebeen given less attention in previous studies.
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Figure 15. Statistics of AI related developments reported in the prior surveys [225].
Besides the unique AI applications cited above, many surveys on AI topics have beenpublished, and each survey has tried to demonstrate the AI uses from different perspectives.
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To aid subsequent research in this regard, we systematically summarize the findings of mostrecent surveys that have focused on AI applications in the context of COVID-19 in Table 4.The analysis presented in Table 4 clearly demonstrates the existing developments in reviewarticles. Our review is an enhanced version of these surveys as we discuss AI’s role morebroadly compared to these studies. This analysis is helpful to quickly and convenientlygrasp the research status of AI in the COVID-19 era.
Table 4. Key findings of most recent surveys that have focused on AI applications in the context ofCOVID-19.
Ref. Pub. Year Review Type Key Findings Concerning COVID-19 Pandemic
Nadeem et al. [226] 2020 Literature survey Discussion of AI applications and data sources to fight against Covid-19 via technologyAbd-Alrazaq et al. [227] 2020 Scoping review Discussion about AI technology use during the ongoing COVID-19 pandemicRaza et al. [228] 2020 Meta-analysis Discussion from broad spectrum of AI to combat COVID-19 by analyzing current SOTA studiesChen et al. [229] 2020 Rapid review Discussion and review of the critical aspect of AI applications for COVID-19 eraEnughwure et al. [230] 2020 Systematic review Analyzed 15 SOTA studies and showed AI has many potentials in combating COVID-19 pandemicChiroma et al. [231] 2020 Bibliometric study Discussion on ML-based technologies to fight the COVID-19 pandemic from multiple perspectivesBullock et al. [232] 2020 Literature review Reviewed many datasets, resources, and tools required to facilitate AI research in the era of COVID-19
Fong et al. [233] 2020 Literature review Discussed the role of AI as a technological enabler from four different perspectives in the era of COVID-19
Latif et al. [234] 2020 Systematic Review Discussed many public datasets/repositories that are used in order to track the spread of COVID-19and mitigation strategies
Chawki et al. [235] 2021 Systematic review Discussed about how AI can be utilized to analyze the social and clinical patterns of a COVID-19outbreak to save people
Gunasekeran et al. [236] 2021 Scoping review Discussed many applications of AI, telehealth, and relevant digital health solutions amidst the COVID-19
Syeda et al. [237] 2021 Systematic review Discussed many studies concerning COVID-19 that have utilized AI-based methods in differentthemes
Zhao et al. [238] 2021 Systematic review Discussed and summarized 50 applications of AI, robotics, and other digital technologies in the era ofCOVID-19
Kamalov et al. [239] 2021 Literature review Discussed AI applications from four perspectives such as medical diagnostics, forecasting, contacttracing, and drug development
Safdari et al. [240] 2021 Scoping review Discussed about determining the most favorite and effective data mining tools in COVID-19 eraNirmala et al. [241] 2021 Literature survey Pinpoints various AI applications that are effective to fight against the COVID-19 pandemic
Rasheed et al. [242] 2021 Literature review Discussed the role of AI from three perspectives such as analyze, prognosis, and tracking of theCOVID-19 cases
Senthilraja et al. [243] 2021 Literature review Discussed and find that AI is useful not only in treatment of infected patients with COVID-19, but alsofor proper health monitoring
Kumar et al. [244] 2021 Literature review Discussed the development of COVID-19 classification tools & drug discovery models for infectedpatients using AI
Chen et al. [245] 2021 Literature survey Investigated the scope of AI in COVID-19 era from the five aspects (i.e., virology, diagnosis, druganalysis, and transmission)
Dogan et al. [246] 2021 Systematic review Analyzed the role of AI/ML for transmission prediction, diagnosis, and drug/vaccine developmentin the pandemic arena
Alafif et al. [247] 2021 Systematic review Analyzed the role of ML/DL towards COVID-19 diagnosis and treatment and discussed findings ofSOTA in the pandemic arena
Singh et al. [248] 2021 Comprehensivereview Discussed about the COVID-19 prevention and detection using different types of biosensors.
Recently, AI has been widely used in COVID-19 drug design and repurposing ondifferent datasets. Tang et al. [249] used AI techniques to predict molecules and leadingcompounds for each target. The dataset used in the study is available at the link (https://github.com/tbwxmu/2019-nCov (accessed on 15 November 2021 )). Similarly, someother studies have also used real-world datasets to find the molecular structures for 3CLpro[250,251]. The data used in these studies can be found at link (https://www.insilico.com/ncov-sprint (accessed on 15 November 2021), https://github.com/ml-jku/sarscov-inhibitors-chemai (accessed on 15 November 2021)). More details about datasets used indrug design can be learned from Chen et al. [245].
5.4. Future Research Directions
The promising research directions that need further exploration from the research anddevelopment point of view are described in Figure 16. Besides the other directions, privacypreservation is one of the hot research topics in the pandemic era [252,253].
Figure 16. Promising research directions in the COVID-19 era leveraging AI.
As shown in Figure 16, fusion of heterogeneous sources data for insight (i.e., contactsof an infected individual, stay points of an individual, and co-relation of the symptoms withunderlying diseases, etc.) finding is a promising avenue for research in the near future [254].During the pandemic, there is an emerging need to secure all phases of the data lifecycle inorder to prevent privacy breaches [255]. However, the existing approaches mainly ensuresecurity of one/two phases. Therefore, more practical and robust AI-powered privacypreserving approaches are needed to secure personal data in the post COVID-19 era. Inthe ongoing pandemic, many AI models have been used for multiple purposes. Moreover,designing low-cost AI models and metrics for COVID-19-like pandemics is an emergingavenue for future research [256]. Apart from the potential avenues of research cited above,performing analytics on the collected data in order to extract insights is a promising area ofresearch due to huge data collection in the ongoing pandemic [257,258]. As the healthcareindustry is aiming to shift from the hospital-centered approach to patient/device-centeredapproach, therefore, AI-based methods for supporting the cause are needed in the nearfuture. Furthermore, exploring the potential of other latest technologies (i.e., blockchain,privacy by design, federated learning, swarm learning, few-short learning, deep/machinelearning, etc.) to serve mankind in an effective way compared to the recent past is an emerg-ing avenue of the research. Finally, critical analysis of AI use from ethical point of view inthe context of ongoing pandemic has become more emergent than ever [259–261]. Besidesthe AI applications cited above, joint use of AI with the IoT/IoMT has become a popularresearch area [262–265]. Recently, researchers have started working on reducing the ‘black-box’ nature of AI models through explain-ability and interpret-ability concepts [266–269].Hence, it is worth exploring the AI use in COVID-19 context from this perspective [270].In addition, most of the AI techniques, especially deep learning techniques, are computa-tionally expensive. Hence, it is an emerging research area to lower the computing burdenof these techniques via pruning and quantization techniques [271–274]. Besides the areascited above, another promising avenue for future research is sentiment analysis of COVID-19-related tweets [275], informative tweets detection related to COVID-19 using deeplearning [276], reviews analysis [277], topic modeling related to COVID-19 aspects [278],COVID-19 pandemic and vaccine-related rumors detection [279], opinion analysis relatedto COVID-19 [280], and awareness prediction [281], to name a few. In these areas, AI canplay a vital role with data stemming from the ongoing pandemic and corresponding surgein SN use across the globe. Therefore, further research and development are likely toexpand in this regard (e.g., AI towards COVID-19) in order to take full advantage of theintegrated technologies to serve humanity.
Lastly, the COVID-19 pandemic has evolved into an endemic, and the new normalmay be to live with the virus for a few more years. Therefore, we must recognize andadvocate caution that such pre-emptive measures (e.g., heavy reliance on the digital tools,
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contactless services, work from home, remote health monitoring with AI tools/applications,distance/remote learning [282], and buying online, to name a few) are ultimately worth-while. Considering the evolution of COVID-19 in various mutations across the globe,preparedness is the key to preventing any public health crisis, and COVID-19-endemiccountries must be ready for the challenges that COVID-19 might bring unanticipatedstrain on medical infrastructure time and again. Furthermore, some tropical diseases (TDs)have been neglected amid the prevalence of COVID-19 because most funds are beingredirected towards COVID-19 [283]. Therefore, COVID-19 effects on medical systems arelong-lasting. The contactless services are increasing significantly amid the ongoing pan-demic, and hybrid healthcare services will likely increase in the post-COVID-19 era [284].Most companies are moving towards zero user interface (ZUI) technologies propelled bythe ongoing pandemic in order to meet the hygiene requirements [285]. The cashless andcontactless smart vending machines are being integrated with the mobile device to reducepeople-to-people contact [286]. Furthermore, new normal activities are constantly emergingin which companies are deploying crisis strategies to retain their stakeholders and business.Companies are re-opening swiftly, focusing more on digital transformations, implementingdigital platforms/tools for improved consumer services and ease in working, to name a fewoperational changes [287]. Besides these technical developments, researchers are emphasiz-ing the need for research in COVID19-induced brain dysfunction (CIBD) to improve themental health of large populations of infected/uninfected individuals [288]. Despite thesehighlighted areas and developments, AI’s role in COVID-19 and post COVID-19 era canoffer a proven method to further strengthen the impact of healthcare services/applicationson population health, which is more necessary than ever in the post-COVID era [289–292].To this end, further developments are needed to lower the effects of this pandemic and toserve mankind effectively in these unanticipated and challenging times.
6. Conclusions and Future Work
This paper has demonstrated the role of data-driven analytics leveraging AI in theera of COVID-19. Specifically, we have discussed the role of AI from six different perspec-tives in the pandemic arena that can assist early researchers to grasp the research statusconveniently. To the best of author knowledge, this is the first article that has presentedvarious possible and demonstrated applications of Artificial Intelligence (AI) related tothe COVID-19 pandemic. It continues to discuss challenges facing the field and proposesfuture avenues of research to follow. The six unique perspectives in which AI’s use/rolewas presented are, (i) AI role in seven different epidemic containment strategies (a.k.anon-pharmaceutical interventions (NPIs)) such as contact tracing, quarantine monitor-ing, social distance monitoring, disclosing patients’ information, reporting symptoms andother data via wearable devices, data collection at the time of check-ups, and mining andanalytics on collected data, (ii) AI role in data life cycle phases (i.e., collection, storage,pre-processing, analytics, use, distribution, archiving, and deletion) employed for epidemichanding in digital solutions, (iii) AI role in performing analytics on heterogeneous types ofdata stemming from this pandemic, (iv) AI role in the healthcare sector in the context ofCOVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AIrole in drug design and repurposing against COVID-19. Furthermore, we discussed thevarious challenges related to data that can hinder the application of AI in the ongoingpandemic period. We have concisely presented the role of emerging technologies otherthan AI, and promising future research directions in the post-COVID-19 era. Furthermore,we have demonstrated the actual use of AI based on available data and utility in thesepandemic times. We have described the findings of recently published SOTA studies thathave demonstrated the role of IoT and various smart sensing technologies in order to fightagainst the COVID-19 pandemic. Furthermore, we discussed the synergy of AI with otheremerging technologies in order to lower the effects of COVID-19 on the general publicand economies. With this comprehensive overview, we aim to update researchers anddevelopers with the existing services of AI, and possible research gaps/opportunities thatAI can provide in the near future, and data-related issues that we may face while applying
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AI models on it. Unfortunately, COVID-19 is creating many complex clinical implicationsfor people having underlying diseases such as diabetes, pneumonia, and heart disease, toname a few. Hence, we may need to deal with the clinical implications emerging from theprognosis of COVID-19 using AI, mastering the “fearful symmetry” [293]. In the future, weaim to explore the development-related challenges of AI in the COVID-19 era. Finally, weintend to explore AI’s role and applications in the post-pandemic era. Recently, federatedanalytics has emerged as a new paradigm for performing analytics without centralizingdata [294–296]. To this end, we intend to explore the role of federated analytics in thepandemic and post-pandemic arena. In addition, studying the role of federated learningin the context of COVID-19 is also a very hot research area in recent times [297–299]. Weintend to explore the role of these emerging technologies to combat this unanticipatedpandemic in future work.
Author Contributions: All authors contributed equally to this work. All authors have read andagreed to the published version of the manuscript.
Funding: This research was supported by the MSIT (Ministry of Science, ICT), Korea, under theHigh-Potential Individuals Global Training Program (No. 2021-0-01532, 50%) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation), and NationalResearch Foundation of Korea (NRF) grant (No.2020R1A2B5B01002145, 50%).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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